Just three months after emerging from stealth, XDOF - a startup that collects real-world teleoperation data for training general-purpose robots - is reportedly in late-stage talks to raise a Series B at a valuation of about $1.2 billion, led by 8VC. Several people with knowledge of the deal said so, and who are we to doubt them?

Co-founded in 2024 by UC Berkeley researchers Philipp Wu (CEO) and Fred Shentu (CTO), XDOF had already raised a $70 million Series A in June, with participation from Thrive Capital, Andreessen Horowitz, Lux, and Spark Capital. Apparently, that wasn't enough. The company wasn't planning to raise again so soon, but with annualized revenue approaching $50 million, VCs came knocking. Because when a startup shows rapid growth, the only logical response is to throw more money at it.

TechCrunch was unable to learn the total capital being raised or whether the valuation includes the new funding. Terms are not final and could change. XDOF and 8VC didn't respond to requests for comment, probably because they were busy counting their potential billions.

The startup aims to build the data pipelines, collection tools, and annotation systems that frontier AI labs and robotics companies can't easily build themselves. Essentially, they're the outsourced data-supply chain for the robotics industry, because who wants to do that tedious work in-house?

Wu's journey began as a PhD student studying how robots learn from large datasets. His research was hampered by a lack of 'large-scale data to work with,' he told TechCrunch in June. So he teamed up with Shentu to create GELLO, a low-cost teleoperation system that lets a human operator control a robotic arm remotely to generate training data. Their work led to an influential paper in robotics, because of course it did.

That research formed the foundation for XDOF, which investors now describe as the Scale AI or Mercor for physical robotics. Unlike LLMs, which initially trained on the entirety of the internet, physical robots don't have an equivalent real-world dataset to draw from. Data collection is a critical bottleneck, and XDOF aims to fix that, one folded shirt at a time.

XDOF is partnering with UC Berkeley's AI Research lab to release what it believes is the largest collection of high-quality robot training data ever assembled, dubbed ABC. Because naming things ABC is both simple and aspirational.

To capture this data, XDOF combines remote robot teleoperation with human collectors who wear sensors to record everyday tasks like folding clothes and flattening boxes. Yes, humans are essentially teaching robots to do chores by wearing sensors and doing the chores themselves. It's a living.

The startup plans to hire and train teams of data collectors worldwide, including teleoperators who steer robots remotely and egocentric operators who wear body sensors to capture movement data. XDOF previously told TechCrunch that it is already working with 20 customers, including several frontier AI labs.

Other startups attempting to collect real-world data for robot training include Mecka AI, as well as human-data platforms expanding beyond LLMs, such as Scale AI and Micro1. Because nothing says 'innovation' like a data gold rush for robots.